Methodology for temporal fault event isolation and identification

ABSTRACT

A method of fault event identification which comprises the steps of receiving a plurality of parameter measurements, performing single fault isolation to establish one of the plurality of parameter measurements as an event start, one of the plurality of parameter measurements as an event detect, and one of the parameter measurements as an event end wherein a latency period extends from the event start to the event detect and a blackout period extends from the event start to the event end, performing multiple fault isolation to establish a first trend line for the plurality of parameter measurements prior to the blackout period and after the blackout period, reprocessing the parameter measurements in the latency period, processing the parameter measurements in the blackout period, calculating a model IC from the reprocessed parameter measurements, calculating a plurality of single fault vectors, calculating an estimate for each of the single fault vectors, calculating a normalized error for each of said plurality of single fault vectors, calculating a polarized error term for each of the plurality of single fault vectors, and selecting the single fault vector with smallest of the calculated normalized errors.

BACKGROUND OF THE INVENTION

[0001] (1) Field of the Invention

[0002] The present invention relates to a method for isolating andidentifying temporal fault events. More specifically, the presentinvention relates to a method of performing single fault isolation anddetection in engine systems, and, if warranted, double fault isolationand detection.

[0003] (2) Description of Related Art

[0004] The goal of gas turbine performance diagnostics is to accuratelydetect, isolate and assess the changes in engine module performance,engine system malfunctions, and instrumentation problems from knowledgeof measured parameters taken along the engine's gas path. Discernableshifts in engine speeds, temperatures, pressures, fuel flow, etc.,provide the requisite information for determining the underlying shiftin engine operation from a presumed reference (nominal) state. Theaforementioned engine performance changes can manifest themselves in oneof two categories: a) gradual (long-term) deterioration or b) rapid(short-term) deterioration.

[0005] Various techniques exist to detect the onset of short term,single fault conditions in a series of parameter measurements. Suchshort term anomalies are usually situated between periods of relativeequilibrium during which long term degradation of engine or modulecomponents exhibit gradual deterioration. In addition, in rareinstances, such short term anomalies are the result of double faultconditions involving the near simultaneous malfunctioning of twocomponents. In order to accurately identify the cause of a single ordouble fault condition there is needed a method whereby measurement datarecorded before, during, and after a fault condition is correctlyidentified as to its nature for use in subsequent fault identification.There is additionally needed a methodology whereby such measurement datamay be analyzed to provide accurate identification of underlying faultconditions.

SUMMARY OF THE INVENTION

[0006] Accordingly, it is an object of the present invention to providea method for isolating and identifying temporal fault events.

[0007] In accordance with the present invention, a method of fault eventidentification which comprises the steps of receiving a plurality ofparameter measurements, performing single fault isolation to establishone of the plurality of parameter measurements as an event start, one ofthe plurality of parameter measurements as an event detect, and one ofthe parameter measurements as an event end wherein a latency periodextends from the event start to the event detect and a blackout periodextends from the event start to the event end, performing multiple faultisolation to establish a first trend line for the plurality of parametermeasurements prior to the blackout period and after the blackout period,reprocessing the parameter measurements in the latency period,processing the parameter measurements in the blackout period,introducing a set of model Influence Coefficients (IC), interpolated atthe power setting being analyzed, calculating a plurality of singlefault vectors, calculating an estimate for each of the single faultvectors, calculating a normalized error for each of the plurality ofsingle fault vectors, calculating a polarized error term for each of theplurality of single fault vectors, and selecting the single fault vectorwith smallest of the calculated normalized errors.

[0008] In accordance with the present invention, a method of fault eventidentification comprises the steps of receiving a plurality of parametermeasurements, performing single fault isolation to establish one of theplurality of parameter measurements as an event start, one of theplurality of parameter measurements as an event detect, and one of theparameter measurements as an event end wherein a latency period extendsfrom the event start to the event detect and a blackout period extendsfrom the event start to the event end, performing multiple faultisolation to establish a first trend line for the plurality of parametermeasurements prior to the blackout period and after the blackout period,reprocessing the parameter measurements in the latency period,processing the parameter measurements in the blackout period,calculating a matrix of Influence Coefficients (IC) from an aero-thermalmodel simulation of the engine, calculating a plurality of single faultvectors, calculating an estimate for each of the single fault vectors,calculating a normalized error for each of the plurality of single faultvectors, calculating a polarized error term for each of the plurality ofsingle fault vectors, and determining if double fault calculation isrequired, calculating a plurality of double fault vectors, calculatingan estimate for each of the plurality of double fault vectors,calculating a normalized error for each of the plurality of double faultvectors, calculating a polarized error term for each of the plurality ofdouble fault vectors, comprising a smallest normalized error for each ofthe plurality of single fault vectors and a smallest normalized errorfor each of the plurality of double fault vectors.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1—An illustration of long-term and short-term deteriorationof a hypothetical measurement parameter.

[0010]FIG. 2—An illustration of the latency period and blackout periodassociated with a single fault condition.

[0011]FIG. 3—A logic diagram of the steps of the present inventionperformed comprising the re-processing of data points in the latencyperiod.

[0012]FIG. 4a—A logic diagram of the steps of the present inventionperformed to calculate the specific model ICs.

[0013]FIG. 4b—A logic diagram of the process steps of the presentinvention by which there is calculated an associated normalized errorfor each single fault estimate and generated polarized error terms.

[0014]FIG. 4c—A logic diagram of the process steps of the presentinvention by which the polarized error terms of the present inventionare processed to select the fault estimate with the minimum computederror.

[0015]FIG. 4d—A logic diagram of the process steps of the presentinvention whereby normalized and polarized error terms for each possibledouble fault is calculated.

[0016]FIG. 4e—A logic diagram of the process steps of the presentinvention whereby it is determined whether to conclude the occurrence ofa single or a double fault condition.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

[0017] The present invention is drawn to isolating short-termdeterioration in the instance where a fault event has been detected in asystem. While described in the following with exemplary reference to gaspath parameters in an engine, the present invention is broadly drawn toencompass any system comprised of a plurality of interoperatingsubsystems or modules which may be measured and quantified duringoperation.

[0018] The problem of detection is one of recognizing a step or ratechange in a gas path parameter or a collection of parameters. Methods todetect an engine performance event, or fault event, by virtue ofdetecting that a shift (or rapid change) in a set of measured engineparameters has occurred at some discrete time k are well known in theart.

[0019] As used herein, the algorithms that address the problem ofestimating a) gradual deterioration and b) rapid (temporal)deterioration in gas turbine system performance are referred to as MFI(Multiple Fault Isolation) and SFI (Single Fault Isolation),respectively. The former implies that all of the engine components(whose shifts in performance are estimated) are deteriorating slowly,whereas the latter implies a concession, i.e. that a rapid trend shiftis most probably due to a single entity (or perhaps two) going awry. Themethodology of the present invention described more fully below allowsfor isolation and identification of single or double faults arising froma rapid deterioration of an engine sub-system.

[0020] It is advantageous to recognize a certain time latency in thedetection process. If one denotes by k_(start) and k_(detect) the startand detection (discrete) times respectively of a temporal event, then itis generally true that k_(detect)≠k_(start). The reason for this is thatit would not be prudent to declare an event detection on the basis ofone data point since the point might be a statistical outlier. Thenotion of a Lateny Period is therefore incorporated into the presentinvention in order to support both MFI and SFI processing. In additionto ascertaining detection time k_(detect), it is also important to knowwhen the event has concluded or stabilized. This time is denoted byk_(end) and the trend detection algorithm, which is monitoring themeasured parameters, estimates this time along with k_(start). As usedherein, “Blackout Period” refers to the period between the start and endof the detected event.

[0021] With reference to FIG. 1, there is presented a graphicalillustration of long-term 11 and short-term 13 deterioration in terms ofa hypothetical trending in an arbitrary measurement parameter Δ.Measurement parameter Δ is preferably a percent or other measured changebetween a current measurement and a reference parameter valuerepresenting a nominal baseline condition. Note that for both long term11 deterioration periods there is computed a trend line 15. Trend lines15 are preferably best fit lines derived from linear regression thatcapture the gradual rate of deterioration in a parameter's measurements,but can also be represented by moving or exponential averages.

[0022] With reference to FIG. 2, there is illustrated a graphicalrepresentation of a short-term event, start 21 (k_(start)), detection 23(k_(detect)) and end time 25 (k_(end)) defining the Latency Period 27and Blackout Period 29.

[0023] In such an exemplary scenario, processing (Fault Detection andIsolation) is taking place at each discrete time point. The defaultisolation process is taken to be the MFI (Multiple Fault Isolation),wherein performance changes (relative to reference) in the modules andsensors being monitored are inferred from the collection of measurementparameter changes (i.e., As from reference) at time k.

[0024] Examples of preferable methods of MFI include, but are notlimited to, statistical based applications using Kalman Filters tomethods employing Artificial Neural Networks.

[0025] Similarly, preferable SFI methods include, but are not limitedto, statistical methods and methods incorporating Artificial NeuralNetworks.

[0026] The algorithm of the present invention possesses a plurality ofproperties as will be described more fully below. The algorithm allowsfor and accommodates the natural latency in the detection of a temporalevent thereby mitigating the effects of concurrent MFI processing. Thealgorithm automatically accommodates single and double fault events. Thealgorithm automatically accommodates any measurement set or subset andit accommodates any set of pre-defined faults.

[0027] The SFI operates on temporal changes, i.e. measurement parameterΔΔs, as indicated in FIG. 2. This provides a performance ΔΔ from theinitiation of the event. The reference in this case is whatever theperformance was at discrete time k_(start)−1. This is what distinguishesa short-term (event) deterioration from long-term (gradual)deterioration.

[0028] In order to describe the short-term event isolation process, thefollowing notation is introduced:

[0029] Z_(i)=(m×1) vector of Measurement Δs at tine k

[0030] Z_(k)(i)=i^(th) component of the vector of Measurement Δs at timek

[0031] ΔΔ_(k)=(m×1) vector of Measurement ΔΔs at time k

[0032] ΔΔ_(k)(i)=i^(th) component of the vector of Measurement ΔΔs attime k

[0033] Δ{circumflex over (x)}_(k) ^((SFI))=(n×1) vector of estimated SFIPerformance ΔΔs at time k

[0034] Δ{circumflex over (x)}_(λ) ^((SFI))(i)=i^(th) component of thevector of estimated Performance ΔΔs at time k

[0035] A_(λ)=(m×1) vector of Measurement Δ Averages at time k

[0036] A_(k)(i)=i^(th) component of the vector of Measurement Δ Averagesat time k

[0037] m=number of Measured Parameters

[0038] n=number of potential SFI event faults

[0039] In order to orchestrate these assessments it is necessary toadhere to the process illustrated in FIG. 3. The inputs into thisprocess are

[0040] Z_(k)=vector of Measurement Δs at time k

[0041] A_(k)=vector of Measurement Δ Averages at time k

[0042] The Measurement Δ Averages are preferably computed by usingexponential averages which are recursive and require minimal memory.Like a rolling average, this form of averaging also tracks parametertrends over time. They are computed as follows:

A _(A) =αA _(k−1)+(1−α)Z _(k)0<α<1

[0043] where the (pre-chosen) constant α, provides the level ofweighting for the past average and current data point.

[0044] The ΔΔ_(k) calculations are made relative to the average leveljust prior to the event initiation, i.e. discrete time k_(start)−1. Thecalculation applies to the entire Blackout Period 29, but differs forthe re-processed Latency Period 27. In the Latency Period 27, a lineartime regression is performed and the ΔΔ_(k) values are computed as thedifference between this regression line and the average level (prior tothe event) as outlined below.

[0045] Let n_(Latency)=number of points in the Latency Period and theregression coefficients within the latency period are calculated asfollows: $\begin{matrix}{{Time}_{avg} = \frac{n_{Latency} + 1}{2}} \\{= {{average}\quad {discrete}\quad {time}\quad {in}\quad {Latency}}} \\{{Time}_{variance} = \left\{ \begin{matrix}0 & {{{if}\quad n_{Latency}} = 1} \\\frac{\left( {n_{Latency} + 1} \right)n_{Latency}}{12} & {{{if}\quad n_{Latency}} > 1}\end{matrix} \right.} \\{= {{variance}\quad {of}\quad {discrete}\quad {time}\quad {in}\quad {Latency}}} \\{{{\overset{\_}{Z}}_{Blackout}(i)} = {\left( \frac{1}{n_{Latency}} \right){\sum\limits_{k = 1}^{n_{Latency}}{Z_{k_{start} + k - 1}(i)}}}} \\{= {{average}\quad {of}\quad i^{th}\quad {parameter}\quad \Delta \quad {in}\quad {Latency}}} \\{{{slope}_{Latency}(i)} = \left\{ \begin{matrix}0 & {{{if}\quad n_{Latency}} = 1} \\{{\left( \frac{12}{{n_{Latency}\left( {n_{Latency} + 1} \right)}\left( {n_{Latency} - 1} \right)} \right){\sum\limits_{k = 1}^{n_{Latency}}\left\lbrack {{kZ}_{k_{start} + k - 1}(i)} \right\rbrack}} - {\left( \frac{6}{n_{Latency} - 1} \right){{\overset{\_}{Z}}_{Blackout}(i)}}} & {{{if}\quad n_{Latency}} > 1}\end{matrix} \right.} \\{= {{regression}\quad {slope}\quad {of}\quad i^{th}\quad {parameter}\quad \Delta \quad {in}\quad {Latency}}}\end{matrix}$

[0046] For each point in the Latency period, the ΔΔ value is calculatedas follows:

[0047] For index=1,2, . . . , n_(Latency)

Level_(index)(i)=slope_(Latency)(i)[index−Time_(avg) ]+{overscore (Z)}_(Blackout)(i)

ΔΔ_(k) _(start) _(+index)(i)=Level_(index)(i)−A _(k) _(start) ⁻¹(i)

i=1,2, . . . ,m

[0048] End For

ΔΔ_(Latency)(i)=ΔΔ_(k) _(start) _(+B) _(Latency) (i)(Final ΔΔin Latency)

[0049] This constitutes a re-processing of the measurement informationfrom the inception of the detected event and the time of detection. Attime points after detection (k>k_(detect)), the ΔΔ_(k) values arecomputed as the difference from the current Measurement A and theaverage level (prior to the event), i.e.

ΔΔ_(k)(i)=Z _(k)(i)−A _(kstart−1)(i), i=1,2, . . . , mfor k>k _(detect)

[0050] Following these steps, there is derived at current time k withina Blackout Period 29 (i.e., k_(start)≦k≦k_(end)), the ΔΔ_(k) vectorquantities, which provide a signature in the Measurement Δ spacerepresentative of the underlying event fault Δ{circumflex over (x)}_(k)^((SFI)).

[0051] Reprocessing within the Blackout Period 29 allows a separatemethod to be applied to calculate the incremental change in performanceΔ{circumflex over (x)}_(k) ^((SFI)) due to the event that can then beadded back to the known (estimated) performance state {circumflex over(x)}_(k) _(start−1) to form a more accurate estimate, i.e.,

{circumflex over (x)} _(k) ={circumflex over (x)} _(K) _(start−1)+Δ{circumflex over (x)} _(k) ^((SKI)) for k _(start) ≦k≦k _(end)

[0052] This mitigates the problem of “fault smearing” in an estimationprocess for an under-determined system where the number of faults islarger than the number of measurements, i.e. n>m.

[0053] The estimation of the incremental performance elementΔ{circumflex over (x)}_(k) ^((SFI)) is illustrated with reference toFIGS. 4a-d. FIG. 4a illustrates the steps performed to calculate thespecific model Influence Coefficients (ICs) represented my matricesH_(e) (engine ICs) and H_(s) (sensor ICs). These matrices consist ofpartial derivatives which relate percent of point changes in theperformance fault parameters to percent of point changes in the measured(observed) parameters and are commonly used for this type of performancediagnostic modeling. The matrix PO represents the so-called statecovariance matrix which is a common design parameter for the KalmanFilter. FIG. 4b illustrates the process steps by which there iscalculated an associated normalized error for each single fault estimateand there is generated polarized error terms. FIG. 4c illustrates theprocess steps by which the polarized error terms are processed to selectthe fault estimate with the minimum computed error. In addition, adetermination is made whether or not to test for a double faultcondition. In the event that double fault detection is warranted, FIG.4d illustrates the process steps whereby normalized and polarized errorterms for each possible double fault is calculated. Lastly, in the eventthat double fault detection was warranted, FIG. 4e illustrates theprocess steps whereby it is determined whether to conclude theoccurrence of a single or a double fault condition.

[0054] The S vector of measurement non-repeatabilities contains as itscomponents the 1-standard deviation precision error expected from theassociated sensor. The last 3 components are the 1-standard deviationprecision errors in the engine inlet pressure and temperature (P₂ and T₂respectively) and the target power setting parameter (which varies withthe engine model).

[0055] The polarity vector is an n×1 vector whose components have values−1, 0, or +1 with the following interpretation:${{polarity}(i)} = \left\{ \begin{matrix}{- 1} & {{if}\quad {the}\quad i^{th}\quad {single}\quad {fault}\quad {can}\quad {only}} \\\quad & {{have}\quad a\quad {negative}\quad {value}} \\0 & {{if}\quad {the}\quad i^{th}\quad {single}\quad {fault}\quad {can}\quad {have}} \\\quad & {{either}\quad {positive}\quad {or}\quad {negative}\quad {value}} \\{+ 1} & {{{if}\quad {the}\quad i^{th}\quad {single}\quad {fault}\quad {can}}\quad} \\\quad & {{only}\quad {have}\quad a\quad {positive}\quad {value}}\end{matrix} \right.$

[0056] The use of the polarity vector in the logic appearing in FIGS.4a-d allows one to disregard single fault assessments whose evaluationare contrary to what is deemed reasonable by assigning them a largeerror. For example, engine Module performance will degrade with time(negative) whereas sensor errors can be either positive or negative.

[0057] Two other vectors appearing in the logic allow the system toadapt automatically to different engine systems. These take the form ofthe Measurement Configuration vector (M_(C)) and the Single FaultConfiguration vector SF_(C). They are defined as follows:$\begin{matrix}{{M_{C}(i)} = \left\{ \begin{matrix}1 & {{if}\quad i^{th}\quad {parameter}\quad {is}\quad {measured}} \\0 & {otherwise}\end{matrix} \right.} \\{{{SF}_{C}(i)} = \left\{ \begin{matrix}1 & {{if}\quad i^{th}\quad {fault}\quad {is}\quad {to}\quad {be}\quad {included}\quad {in}\quad {the}\quad {search}\quad {set}} \\0 & {otherwise}\end{matrix} \right.}\end{matrix}$

[0058] It is apparent that there has been provided in accordance withthe present invention a method of performing single fault isolation anddetection in engine systems. While the present invention has beendescribed in the context of specific embodiments thereof, otheralternatives, modifications, and variations will become apparent tothose skilled in the art having read the foregoing description.Accordingly, it is intended to embrace those alternatives,modifications, and variations as fall within the broad scope of theappended claims.

What is claimed is:
 1. A method of fault event identification,comprising the steps of: receiving a plurality of parametermeasurements; performing single fault isolation to establish one of saidplurality of parameter measurements as an event start, one of saidplurality of parameter measurements as an event detect, and one of saidparameter measurements as an event end wherein a latency period extendsfrom said event start to said event detect and a blackout period extendsfrom said event start to said event end; performing multiple faultisolation to establish a first trend line for said plurality ofparameter measurements prior to said blackout period and after saidblackout period; reprocessing said parameter measurements in saidlatency period; processing said parameter measurements in said blackoutperiod; calculating a model IC from aero-thermal model simulation of theengine; calculating a plurality of single fault vectors; calculating anestimate for each of said single fault vectors; calculating a normalizederror for each of said plurality of single fault vectors; calculating apolarized error term for each of said plurality of single fault vectors;and selecting said single fault vector with smallest of said calculatednormalized errors.
 2. The method of claim 1 wherein each of saidplurality of parameter measurements comprises a differential of acurrent measurement and an immediately preceding measurement.
 3. Themethod of claim 2 wherein each of said plurality of parametermeasurement is a percentage differential.
 4. The method of claim 1wherein said performing single fault isolation comprises performing astatistical analysis.
 5. The method of claim 1, wherein said performingsingle fault isolation comprises applying Kalman Filter parameterestimation.
 6. The method of claim 1, wherein said plurality ofparameter measurements are derived from an engine.
 7. The method ofclaim 1, wherein said reprocessing said parameter measurements comprisesthe steps of: computing a linear regression line from said plurality ofparameter measurements comprising said latency period; and recomputingeach of said plurality of measurements comprising said latency period asa difference between said linear regression line and an average levelprior to said event start.
 8. A method of fault event identification,comprising the steps of: receiving a plurality of parametermeasurements; performing single fault isolation to establish one of saidplurality of parameter measurements as an event start, one of saidplurality of parameter measurements as an event detect, and one of saidparameter measurements as an event end wherein a latency period extendsfrom said event start to said event detect and a blackout period extendsfrom said event start to said event end; performing multiple faultisolation to establish a first trend line for said plurality ofparameter measurements prior to said blackout period and after saidblackout period; reprocessing said parameter measurements in saidlatency period; processing said parameter measurements in said blackoutperiod; calculating a model IC from said reprocessed parametermeasurements; calculating a plurality of single fault vectors;calculating an estimate for each of said single fault vectors;calculating a normalized error for each of said plurality of singlefault vectors; calculating a polarized error term for each of saidplurality of single fault vectors; and determining if double faultcalculation is required; calculating a plurality of double faultvectors; calculating an estimate for each of said plurality of doublefault vectors; calculating a normalized error for each of said pluralityof double fault vectors; calculating a polarized error term for each ofsaid plurality of double fault vectors; comprising a smallest normalizederror for each of said plurality of single fault vectors and a smallestnormalized error for each of said plurality of double fault vectors. 9.The method of claim 6 comprising the additional steps of: selecting thesmaller of said smallest normalized error for each of said plurality ofsingle fault vectors and said smallest normalized error for each of saidplurality of double fault vectors.
 10. The method of claim 8 whereineach of said plurality of parameter measurements comprises adifferential of a current measurement and an immediately precedingmeasurement.
 11. The method of claim 10 wherein each of said pluralityof parameter measurement is a percentage differential.
 12. The method ofclaim 8 wherein said performing single fault isolation comprisesperforming a statistical analysis.
 13. The method of claim 8, whereinsaid performing single fault isolation comprises applying Kalman Filterparameter estimation.
 14. The method of claim 8, wherein said pluralityof parameter measurements are derived from an engine.
 15. The method ofclaim 8, wherein said reprocessing said parameter measurements comprisesthe steps of: computing a linear regression line from said plurality ofparameter measurements comprising said latency period; and recomputingeach of said plurality of measurements comprising said latency period asa difference between said linear regression line and an average levelprior to said event start.